Identification of Possible Causative Agents in a Polymedicated Patient Presenting With Toxic Epidermal Necrolysis
Bibliographic record
Abstract
PURPOSE: To present the pharmacological evaluation process in a case of a polymedicated patient presenting with toxic epidermal necrolysis (TEN). SUMMARY: A 75-year-old Caucasian polymedicated woman had been treated for hip pain with nonsteroidal anti-inflammatory drugs and pregabalin in the months preceding the apparition of an expanding papulo-erythematous rash. She had also started using new medicated eye drops for glaucoma. She presented to the emergency department of a regional hospital where all of her medications were stopped. The patient was transferred and admitted to a tertiary-care teaching hospital's specialized burn unit for significant cutaneous detachment. It was estimated that 70% to 80% of the body surface area was affected. Skin biopsy showed keratinocyte necrosis with a partial detachment of the epidermis leading to a diagnosis of TEN. The reaction ceased to progress 2 days after the discontinuation of her medications. A complete reepithelialization was objectified after 10 days. A series of steps were followed by the hospital pharmacist to determine which drugs were the most probable culprits. A complete pharmacological history was obtained and a timeline for medication use in the 3 months preceding rash apparition was established. A review of the literature was done to determine the drugs' relationships to Steven-Johnson syndrome or TEN. Using the algorithm of drug causality for epidermal necrolysis (ALDEN) score, it was determined that naproxen, pregabalin, and brinzolamide-timolol drops were all possible culprits. CONCLUSION: A systematic method for pharmacological evaluation of a polymedicated patient with TEN is presented. Naproxen, pregabalin, and brinzolamide-timolol drops were all retained as possible culprits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".